Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder
This work addresses efficient storage for time series data in domains like finance and medicine, but it is incremental as it builds on existing autoencoder and segmentation techniques.
The paper tackles time series compression by proposing an adaptive piecewise recurrent autoencoder that partitions time series into segments with similar total variation and uses RNNs for encoding and decoding, achieving competitive compression ratios on real-world datasets.
Time series account for a large proportion of the data stored in financial, medical and scientific databases. The efficient storage of time series is important in practical applications. In this paper, we propose a novel compression scheme for time series. The encoder and decoder are both composed by recurrent neural networks (RNN) such as long short-term memory (LSTM). There is an autoencoder between encoder and decoder, which encodes the hidden state and input together and decodes them at the decoder side. Moreover, we pre-process the original time series by partitioning it into segments with various lengths which have similar total variation. The experimental study shows that the proposed algorithm can achieve competitive compression ratio on real-world time series.